It is a centralized repository for electronic health records and clinical data that has been gathered from various sources, processed, examined, and distributed to enable data-driven decisions in a matter of minutes. They assist in converting organizational data into easily accessible, useful information that promotes quality advancement in clinical outcomes, patient experiences, and operational effectiveness. We as your experienced data analytics company can assist you in developing data and metrics as valuable assets to improve standardization and transparency.
ReapMind develops enterprise data warehouses, which serve as a key component of a healthcare BI solution that includes the following components:
Data source layer contains healthcare data from internal and external data sources (ERP, EHR/EMR, CRM, claims management systems, pharmacy management systems, and so on).
Intermediate temporary storage where healthcare data is extracted, transformed, and loaded (ETL) or extracted, loaded, and transform (ELT).
consists of centralized structured storage. It may also include data marts, which are subsets of healthcare DWH oriented to a specific business line (HR, accounting, etc.) or department (radiology, intensive care, pediatrics, etc.).
are tools for business analytics, data mining, data reporting, and visualization.
ReapMind offers medical data warehouse solutions, each of which has a different functionality depending on the customer. We've listed the features below that healthcare organizations we work with frequently ask for:
While an enterprise DWH for healthcare can store highly structured data, semi-structured and unstructured data (manual patient records, image-based test reports, practitioner’s notes, etc.) can be stored more affordably in a data lake.
The data that is stored in the data lake is used to create the machine learning models, such as forecasting healthcare demand.
Business Intelligence Applications
Healthcare organizations can use a self-service BI system to visualize, analyze, and report the medical data arranged in the EDW in a flexible and independent manner. This makes it possible for key decision-makers to quickly and simply receive analytics insights.
Based on years of experience in designing and implementing data warehousing solutions, ReapMind has identified a set of factors that, when taken into account, help maximize ROI for DWH projects
150+ Successful Healthcare Projects delivered
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In order to effectively address new data analytics goals, it is possible to instantly upload any kind (structured, semi-structured, unstructured) and volume of healthcare-related data.
Following are some best practices that ReapMind highlights:
ReapMind empowers conducting an extensive analysis of the data warehouse system and developing strong data governance practices to ensure the high quality of the data delivered from a variety of data sources. Various encoding formats, attribute measurements from various data source systems, conflicting key fields, etc. are examples of common data quality challenges that this will aid in addressing.
Our data warehouse implementation process can be divided into four steps.
ReapMind has assisted several healthcare organizations in expanding and revolutionizing their care-delivery systems through end-to-end data warehousing services. We'll work with you to implement a reliable EDW that drives informed decisions and profitable actions in the long run because data accuracy is critical to running any healthcare facility efficiently.
We offer our clients a healthcare data warehouse that includes:
A technically sound and secure infrastructure
Leading-edge data analysis tools
The ability to integrate all types of healthcare data for SQL querying.
Ongoing support from data science experts · Storage and computing resource scalability
EMR, EHR, HIE, and CDS systems are seamlessly integrated.
Cleaning and migrating healthcare data